Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	96,321
2	South Dakota	83,476
3	Iowa	68,078
4	Wisconsin	65,212
5	Nebraska	59,925
6	Utah	55,964
7	Montana	52,796
8	Illinois	52,546
9	Idaho	52,377
10	Wyoming	50,851
11	Kansas	49,713
12	Tennessee	49,440
13	Minnesota	49,037
14	Arkansas	48,442
15	Mississippi	48,344
16	Alabama	47,740
17	Rhode Island	47,739
18	Louisiana	47,573
19	Missouri	46,980
20	Indiana	45,092
21	Oklahoma	44,952
22	Nevada	44,371
23	Florida	43,986
24	Arizona	41,654
25	Georgia	40,962
26	Texas	40,826
27	South Carolina	40,311
28	New Mexico	40,131
29	Alaska	38,450
30	Kentucky	36,655
31	Colorado	35,351
32	New Jersey	35,058
33	Michigan	34,067
34	Delaware	33,078
35	North Carolina	32,386
36	New York	31,196
37	Ohio	31,080
38	Maryland	30,458
39	Massachusetts	30,268
40	Connecticut	29,938
41	California	28,756
42	District of Columbia	28,749
43	Virginia	25,896
44	Puerto Rico	25,617
45	Pennsylvania	25,015
46	West Virginia	22,941
47	Washington	20,398
48	Oregon	15,727
49	New Hampshire	13,268
50	Hawaii	12,292
51	Maine	7,844
52	Vermont	5,952

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,478
2	Wyoming	1,325
3	Minnesota	1,170
4	New Mexico	1,120
5	South Dakota	1,046
6	Montana	961
7	Iowa	937
8	Indiana	921
9	Utah	912
10	Oklahoma	894
11	Kansas	870
12	Wisconsin	830
13	Rhode Island	809
14	Ohio	795
15	Illinois	790
16	Alaska	786
17	Colorado	780
18	Nebraska	734
19	Nevada	711
20	Missouri	670
21	Michigan	654
22	Idaho	607
23	Kentucky	607
24	Tennessee	603
25	Connecticut	492
26	Pennsylvania	491
27	West Virginia	489
28	Delaware	477
29	Arizona	472
30	Arkansas	472
31	New Jersey	458
32	Alabama	387
33	Mississippi	386
34	Maryland	380
35	Massachusetts	371
36	Washington	353
37	Texas	351
38	California	335
39	Florida	330
40	Oregon	328
41	Louisiana	319
42	Georgia	318
43	North Carolina	306
44	New Hampshire	305
45	Virginia	300
46	Puerto Rico	299
47	New York	295
48	South Carolina	284
49	District of Columbia	227
50	Maine	145
51	Vermont	136
52	Hawaii	88

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,888
2	New York	1,737
3	Massachusetts	1,527
4	Connecticut	1,366
5	Louisiana	1,351
6	Mississippi	1,235
7	Rhode Island	1,235
8	North Dakota	1,118
9	Illinois	958
10	District of Columbia	952
11	South Dakota	925
12	Michigan	894
13	Arizona	888
14	Georgia	845
15	Florida	841
16	South Carolina	832
17	Indiana	792
18	Arkansas	790
19	Pennsylvania	775
20	Delaware	772
21	Maryland	735
22	Texas	729
23	Alabama	705
24	Iowa	704
25	New Mexico	668
26	Nevada	659
27	Tennessee	622
28	Missouri	602
29	Minnesota	588
30	Montana	575
31	Wisconsin	545
32	Ohio	515
33	Kansas	499
34	Nebraska	495
35	Colorado	489
36	Idaho	486
37	North Carolina	483
38	California	474
39	Virginia	461
40	Kentucky	426
41	Oklahoma	416
42	New Hampshire	376
43	West Virginia	372
44	Washington	364
45	Wyoming	349
46	Puerto Rico	323
47	Utah	249
48	Oregon	196
49	Hawaii	163
50	Alaska	132
51	Maine	131
52	Vermont	100

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	29
2	Wyoming	14
3	Montana	12
4	North Dakota	12
5	New Mexico	11
6	Illinois	8
7	Arkansas	7
8	Nebraska	7
9	Indiana	6
10	Iowa	6
11	Minnesota	6
12	Kansas	5
13	Michigan	5
14	West Virginia	5
15	Connecticut	4
16	Nevada	4
17	Pennsylvania	4
18	Puerto Rico	4
19	Rhode Island	4
20	Tennessee	4
21	Colorado	3
22	Florida	3
23	Idaho	3
24	Kentucky	3
25	Louisiana	3
26	Mississippi	3
27	Oklahoma	3
28	South Carolina	3
29	Texas	3
30	Wisconsin	3
31	Delaware	2
32	Georgia	2
33	Hawaii	2
34	Maryland	2
35	Massachusetts	2
36	New Jersey	2
37	North Carolina	2
38	Utah	2
39	Arizona	1
40	California	1
41	District of Columbia	1
42	Missouri	1
43	New Hampshire	1
44	New York	1
45	Ohio	1
46	Oregon	1
47	Virginia	1
48	Washington	1
49	Alabama	0
50	Alaska	0
51	Maine	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Norton	Kansas	196,419	1	99
Bon Homme	South Dakota	191,277	2	99
Lincoln	Arkansas	183,738	3	99
Buffalo	South Dakota	183,486	4	99
Chattahoochee	Georgia	178,876	5	99
Davidson	Tennessee	62,516	486	84
Richland	South Carolina	47,614	1054	66
York	South Carolina	32,369	1959	37
Orange	California	23,106	2478	21
Pierce	Washington	18,153	2684	14

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	710	1171	62
Davidson	Tennessee	586	1432	54
Orange	California	489	1668	46
York	South Carolina	470	1722	45
Pierce	Washington	327	2151	31

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.